Calibration-free fusion of step counter and wireless fingerprints for indoor localization

In order to improve the accuracy of fingerprint-based localization, one may fuse step counter measurement with location estimation. Previous works on this often require a pre-calibrating the step counter with training sequence or explicit user input, which is inconvenient for practical deployment. Some assume conditional independence on successive sensor readings, which achieves unsatisfactory accuracy in complex and noisy environment. Some other works need a calibration process for RSSI measurement consistency if different devices are used for offline fingerprint collection and online location query. We propose SLAC, a fingerprint positioning framework which simultaneously localizes the target and calibrates the system. SLAC is calibration-free, and works transparently for heterogeneous devices and users. It is based on a novel formulation embedded with a specialized particle filter, where location estimations, wireless signals and user motion are jointly optimized with resultant consistent and correct model parameters. Extensive experimental trials at HKUST campus and Hong Kong International Airport further confirm that SLAC accommodates device heterogeneity, and achieves significantly lower errors compared with other state-of-the-art algorithms.